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  <div class="section" id="torchfm-model">
<h1>torchfm.model<a class="headerlink" href="#torchfm-model" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-torchfm.model.afi">
<span id="torchfm-model-afi"></span><h2>torchfm.model.afi<a class="headerlink" href="#module-torchfm.model.afi" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.afi.AutomaticFeatureInteractionModel">
<em class="property">class </em><code class="descclassname">torchfm.model.afi.</code><code class="descname">AutomaticFeatureInteractionModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>num_heads</em>, <em>num_layers</em>, <em>mlp_dims</em>, <em>dropouts</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/afi.html#AutomaticFeatureInteractionModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.afi.AutomaticFeatureInteractionModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of AutoInt.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>W Song, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2018.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.afi.AutomaticFeatureInteractionModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/afi.html#AutomaticFeatureInteractionModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.afi.AutomaticFeatureInteractionModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.afm">
<span id="torchfm-model-afm"></span><h2>torchfm.model.afm<a class="headerlink" href="#module-torchfm.model.afm" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.afm.AttentionalFactorizationMachineModel">
<em class="property">class </em><code class="descclassname">torchfm.model.afm.</code><code class="descname">AttentionalFactorizationMachineModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>attn_size</em>, <em>dropouts</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/afm.html#AttentionalFactorizationMachineModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.afm.AttentionalFactorizationMachineModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of Attentional Factorization Machine.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.afm.AttentionalFactorizationMachineModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/afm.html#AttentionalFactorizationMachineModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.afm.AttentionalFactorizationMachineModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.dcn">
<span id="torchfm-model-dcn"></span><h2>torchfm.model.dcn<a class="headerlink" href="#module-torchfm.model.dcn" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.dcn.DeepCrossNetworkModel">
<em class="property">class </em><code class="descclassname">torchfm.model.dcn.</code><code class="descname">DeepCrossNetworkModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>num_layers</em>, <em>mlp_dims</em>, <em>dropout</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/dcn.html#DeepCrossNetworkModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.dcn.DeepCrossNetworkModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of Deep &amp; Cross Network.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>R Wang, et al. Deep &amp; Cross Network for Ad Click Predictions, 2017.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.dcn.DeepCrossNetworkModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/dcn.html#DeepCrossNetworkModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.dcn.DeepCrossNetworkModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.dfm">
<span id="torchfm-model-dfm"></span><h2>torchfm.model.dfm<a class="headerlink" href="#module-torchfm.model.dfm" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.dfm.DeepFactorizationMachineModel">
<em class="property">class </em><code class="descclassname">torchfm.model.dfm.</code><code class="descname">DeepFactorizationMachineModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>mlp_dims</em>, <em>dropout</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/dfm.html#DeepFactorizationMachineModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.dfm.DeepFactorizationMachineModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of DeepFM.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.dfm.DeepFactorizationMachineModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/dfm.html#DeepFactorizationMachineModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.dfm.DeepFactorizationMachineModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.ffm">
<span id="torchfm-model-ffm"></span><h2>torchfm.model.ffm<a class="headerlink" href="#module-torchfm.model.ffm" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.ffm.FieldAwareFactorizationMachineModel">
<em class="property">class </em><code class="descclassname">torchfm.model.ffm.</code><code class="descname">FieldAwareFactorizationMachineModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/ffm.html#FieldAwareFactorizationMachineModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.ffm.FieldAwareFactorizationMachineModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of Field-aware Factorization Machine.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.ffm.FieldAwareFactorizationMachineModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/ffm.html#FieldAwareFactorizationMachineModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.ffm.FieldAwareFactorizationMachineModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.fm">
<span id="torchfm-model-fm"></span><h2>torchfm.model.fm<a class="headerlink" href="#module-torchfm.model.fm" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.fm.FactorizationMachineModel">
<em class="property">class </em><code class="descclassname">torchfm.model.fm.</code><code class="descname">FactorizationMachineModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/fm.html#FactorizationMachineModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.fm.FactorizationMachineModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of Factorization Machine.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>S Rendle, Factorization Machines, 2010.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.fm.FactorizationMachineModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/fm.html#FactorizationMachineModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.fm.FactorizationMachineModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.fnfm">
<span id="torchfm-model-fnfm"></span><h2>torchfm.model.fnfm<a class="headerlink" href="#module-torchfm.model.fnfm" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.fnfm.FieldAwareNeuralFactorizationMachineModel">
<em class="property">class </em><code class="descclassname">torchfm.model.fnfm.</code><code class="descname">FieldAwareNeuralFactorizationMachineModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>mlp_dims</em>, <em>dropouts</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/fnfm.html#FieldAwareNeuralFactorizationMachineModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.fnfm.FieldAwareNeuralFactorizationMachineModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of Field-aware Neural Factorization Machine.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>L Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.fnfm.FieldAwareNeuralFactorizationMachineModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/fnfm.html#FieldAwareNeuralFactorizationMachineModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.fnfm.FieldAwareNeuralFactorizationMachineModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.fnn">
<span id="torchfm-model-fnn"></span><h2>torchfm.model.fnn<a class="headerlink" href="#module-torchfm.model.fnn" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.fnn.FactorizationSupportedNeuralNetworkModel">
<em class="property">class </em><code class="descclassname">torchfm.model.fnn.</code><code class="descname">FactorizationSupportedNeuralNetworkModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>mlp_dims</em>, <em>dropout</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/fnn.html#FactorizationSupportedNeuralNetworkModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.fnn.FactorizationSupportedNeuralNetworkModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of Neural Factorization Machine.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.fnn.FactorizationSupportedNeuralNetworkModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/fnn.html#FactorizationSupportedNeuralNetworkModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.fnn.FactorizationSupportedNeuralNetworkModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.lr">
<span id="torchfm-model-lr"></span><h2>torchfm.model.lr<a class="headerlink" href="#module-torchfm.model.lr" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.lr.LogisticRegressionModel">
<em class="property">class </em><code class="descclassname">torchfm.model.lr.</code><code class="descname">LogisticRegressionModel</code><span class="sig-paren">(</span><em>field_dims</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/lr.html#LogisticRegressionModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.lr.LogisticRegressionModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of Logistic Regression.</p>
<dl class="method">
<dt id="torchfm.model.lr.LogisticRegressionModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/lr.html#LogisticRegressionModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.lr.LogisticRegressionModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.nfm">
<span id="torchfm-model-nfm"></span><h2>torchfm.model.nfm<a class="headerlink" href="#module-torchfm.model.nfm" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.nfm.NeuralFactorizationMachineModel">
<em class="property">class </em><code class="descclassname">torchfm.model.nfm.</code><code class="descname">NeuralFactorizationMachineModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>mlp_dims</em>, <em>dropouts</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/nfm.html#NeuralFactorizationMachineModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.nfm.NeuralFactorizationMachineModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of Neural Factorization Machine.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.nfm.NeuralFactorizationMachineModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/nfm.html#NeuralFactorizationMachineModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.nfm.NeuralFactorizationMachineModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.pnn">
<span id="torchfm-model-pnn"></span><h2>torchfm.model.pnn<a class="headerlink" href="#module-torchfm.model.pnn" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.pnn.ProductNeuralNetworkModel">
<em class="property">class </em><code class="descclassname">torchfm.model.pnn.</code><code class="descname">ProductNeuralNetworkModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>mlp_dims</em>, <em>dropout</em>, <em>method='inner'</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/pnn.html#ProductNeuralNetworkModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.pnn.ProductNeuralNetworkModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of inner/outer Product Neural Network.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.pnn.ProductNeuralNetworkModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/pnn.html#ProductNeuralNetworkModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.pnn.ProductNeuralNetworkModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.wd">
<span id="torchfm-model-wd"></span><h2>torchfm.model.wd<a class="headerlink" href="#module-torchfm.model.wd" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.wd.WideAndDeepModel">
<em class="property">class </em><code class="descclassname">torchfm.model.wd.</code><code class="descname">WideAndDeepModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>mlp_dims</em>, <em>dropout</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/wd.html#WideAndDeepModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.wd.WideAndDeepModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of wide and deep learning.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>HT Cheng, et al. Wide &amp; Deep Learning for Recommender Systems, 2016.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.wd.WideAndDeepModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/wd.html#WideAndDeepModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.wd.WideAndDeepModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-torchfm.model.xdfm">
<span id="torchfm-model-xdfm"></span><h2>torchfm.model.xdfm<a class="headerlink" href="#module-torchfm.model.xdfm" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchfm.model.xdfm.ExtremeDeepFactorizationMachineModel">
<em class="property">class </em><code class="descclassname">torchfm.model.xdfm.</code><code class="descname">ExtremeDeepFactorizationMachineModel</code><span class="sig-paren">(</span><em>field_dims</em>, <em>embed_dim</em>, <em>mlp_dims</em>, <em>dropout</em>, <em>cross_layer_sizes</em>, <em>split_half=True</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/xdfm.html#ExtremeDeepFactorizationMachineModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.xdfm.ExtremeDeepFactorizationMachineModel" title="Permalink to this definition">¶</a></dt>
<dd><p>A pytorch implementation of xDeepFM.</p>
<dl class="simple">
<dt>Reference:</dt><dd><p>J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.</p>
</dd>
</dl>
<dl class="method">
<dt id="torchfm.model.xdfm.ExtremeDeepFactorizationMachineModel.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_build/_modules/torchfm/model/xdfm.html#ExtremeDeepFactorizationMachineModel.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchfm.model.xdfm.ExtremeDeepFactorizationMachineModel.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Long tensor of size <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_fields)</span></code></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>
</div>


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